Overview

Dataset statistics

Number of variables24
Number of observations129487
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory61.0 MiB
Average record size in memory494.2 B

Variable types

Numeric18
Categorical6

Alerts

Inflight wifi service is highly correlated with Ease of Online bookingHigh correlation
Ease of Online booking is highly correlated with Inflight wifi serviceHigh correlation
Food and drink is highly correlated with Seat comfort and 2 other fieldsHigh correlation
Seat comfort is highly correlated with Food and drink and 2 other fieldsHigh correlation
Inflight entertainment is highly correlated with Food and drink and 2 other fieldsHigh correlation
On-board service is highly correlated with Baggage handling and 1 other fieldsHigh correlation
Baggage handling is highly correlated with On-board service and 1 other fieldsHigh correlation
Inflight service is highly correlated with On-board service and 1 other fieldsHigh correlation
Cleanliness is highly correlated with Food and drink and 2 other fieldsHigh correlation
Departure Delay in Minutes is highly correlated with Arrival Delay in MinutesHigh correlation
Arrival Delay in Minutes is highly correlated with Departure Delay in MinutesHigh correlation
Inflight wifi service is highly correlated with Ease of Online bookingHigh correlation
Ease of Online booking is highly correlated with Inflight wifi serviceHigh correlation
Food and drink is highly correlated with Seat comfort and 2 other fieldsHigh correlation
Seat comfort is highly correlated with Food and drink and 2 other fieldsHigh correlation
Inflight entertainment is highly correlated with Food and drink and 2 other fieldsHigh correlation
On-board service is highly correlated with Baggage handling and 1 other fieldsHigh correlation
Baggage handling is highly correlated with On-board service and 1 other fieldsHigh correlation
Inflight service is highly correlated with On-board service and 1 other fieldsHigh correlation
Cleanliness is highly correlated with Food and drink and 2 other fieldsHigh correlation
Departure Delay in Minutes is highly correlated with Arrival Delay in MinutesHigh correlation
Arrival Delay in Minutes is highly correlated with Departure Delay in MinutesHigh correlation
Inflight wifi service is highly correlated with Ease of Online bookingHigh correlation
Ease of Online booking is highly correlated with Inflight wifi serviceHigh correlation
Food and drink is highly correlated with Inflight entertainment and 1 other fieldsHigh correlation
Seat comfort is highly correlated with Inflight entertainment and 1 other fieldsHigh correlation
Inflight entertainment is highly correlated with Food and drink and 2 other fieldsHigh correlation
On-board service is highly correlated with Inflight serviceHigh correlation
Baggage handling is highly correlated with Inflight serviceHigh correlation
Inflight service is highly correlated with On-board service and 1 other fieldsHigh correlation
Cleanliness is highly correlated with Food and drink and 2 other fieldsHigh correlation
Departure Delay in Minutes is highly correlated with Arrival Delay in MinutesHigh correlation
Arrival Delay in Minutes is highly correlated with Departure Delay in MinutesHigh correlation
Type of Travel is highly correlated with ClassHigh correlation
satisfaction is highly correlated with ClassHigh correlation
Class is highly correlated with Type of Travel and 1 other fieldsHigh correlation
Type of Travel is highly correlated with satisfactionHigh correlation
Inflight wifi service is highly correlated with Departure/Arrival time convenient and 4 other fieldsHigh correlation
Departure/Arrival time convenient is highly correlated with Inflight wifi service and 2 other fieldsHigh correlation
Ease of Online booking is highly correlated with Inflight wifi service and 3 other fieldsHigh correlation
Gate location is highly correlated with Inflight wifi service and 2 other fieldsHigh correlation
Food and drink is highly correlated with Seat comfort and 2 other fieldsHigh correlation
Online boarding is highly correlated with Inflight wifi service and 2 other fieldsHigh correlation
Seat comfort is highly correlated with Food and drink and 4 other fieldsHigh correlation
Inflight entertainment is highly correlated with Food and drink and 5 other fieldsHigh correlation
On-board service is highly correlated with Inflight entertainment and 3 other fieldsHigh correlation
Leg room service is highly correlated with On-board service and 1 other fieldsHigh correlation
Baggage handling is highly correlated with Inflight entertainment and 2 other fieldsHigh correlation
Checkin service is highly correlated with Seat comfortHigh correlation
Inflight service is highly correlated with Inflight entertainment and 3 other fieldsHigh correlation
Cleanliness is highly correlated with Food and drink and 3 other fieldsHigh correlation
Departure Delay in Minutes is highly correlated with Arrival Delay in MinutesHigh correlation
Arrival Delay in Minutes is highly correlated with Departure Delay in MinutesHigh correlation
satisfaction is highly correlated with Type of Travel and 2 other fieldsHigh correlation
Inflight wifi service has 3908 (3.0%) zeros Zeros
Departure/Arrival time convenient has 6664 (5.1%) zeros Zeros
Ease of Online booking has 5666 (4.4%) zeros Zeros
Departure Delay in Minutes has 73209 (56.5%) zeros Zeros
Arrival Delay in Minutes has 72753 (56.2%) zeros Zeros

Reproduction

Analysis started2022-01-08 11:29:42.155682
Analysis finished2022-01-08 11:30:24.262973
Duration42.11 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

Distinct103656
Distinct (%)80.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44158.97348
Minimum0
Maximum103903
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1011.7 KiB
2022-01-08T06:30:24.337251image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3246
Q116230
median38966
Q371440.5
95-th percentile97412.7
Maximum103903
Range103903
Interquartile range (IQR)55210.5

Descriptive statistics

Standard deviation31209.52633
Coefficient of variation (CV)0.7067538911
Kurtosis-1.222284219
Mean44158.97348
Median Absolute Deviation (MAD)25981
Skewness0.3321172498
Sum5718012999
Variance974034534
MonotonicityNot monotonic
2022-01-08T06:30:24.426592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02
 
< 0.1%
173052
 
< 0.1%
173152
 
< 0.1%
173142
 
< 0.1%
173132
 
< 0.1%
173122
 
< 0.1%
173112
 
< 0.1%
173102
 
< 0.1%
173092
 
< 0.1%
173082
 
< 0.1%
Other values (103646)129467
> 99.9%
ValueCountFrequency (%)
02
< 0.1%
12
< 0.1%
22
< 0.1%
32
< 0.1%
42
< 0.1%
52
< 0.1%
62
< 0.1%
72
< 0.1%
82
< 0.1%
92
< 0.1%
ValueCountFrequency (%)
1039031
< 0.1%
1039021
< 0.1%
1039011
< 0.1%
1039001
< 0.1%
1038991
< 0.1%
1038981
< 0.1%
1038971
< 0.1%
1038961
< 0.1%
1038951
< 0.1%
1038941
< 0.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.7 MiB
Female
65703 
Male
63784 

Length

Max length6
Median length6
Mean length5.014820021
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowFemale

Common Values

ValueCountFrequency (%)
Female65703
50.7%
Male63784
49.3%

Length

2022-01-08T06:30:24.511385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-08T06:30:24.562733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
female65703
50.7%
male63784
49.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Customer Type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.8 MiB
Loyal Customer
105773 
disloyal Customer
23714 

Length

Max length17
Median length14
Mean length14.54941423
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLoyal Customer
2nd rowLoyal Customer
3rd rowdisloyal Customer
4th rowLoyal Customer
5th rowLoyal Customer

Common Values

ValueCountFrequency (%)
Loyal Customer105773
81.7%
disloyal Customer23714
 
18.3%

Length

2022-01-08T06:30:24.609835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-08T06:30:24.654582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
customer129487
50.0%
loyal105773
40.8%
disloyal23714
 
9.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Age
Real number (ℝ≥0)

Distinct75
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.42876119
Minimum7
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1011.7 KiB
2022-01-08T06:30:24.710506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile15
Q127
median40
Q351
95-th percentile64
Maximum85
Range78
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.11759674
Coefficient of variation (CV)0.3834154634
Kurtosis-0.718736869
Mean39.42876119
Median Absolute Deviation (MAD)12
Skewness-0.003376447203
Sum5105512
Variance228.5417312
MonotonicityNot monotonic
2022-01-08T06:30:24.795539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
393681
 
2.8%
253501
 
2.7%
403203
 
2.5%
443099
 
2.4%
413081
 
2.4%
423011
 
2.3%
432936
 
2.3%
232932
 
2.3%
452927
 
2.3%
222926
 
2.3%
Other values (65)98190
75.8%
ValueCountFrequency (%)
7682
0.5%
8793
0.6%
9852
0.7%
10820
0.6%
11831
0.6%
12793
0.6%
13805
0.6%
14857
0.7%
151003
0.8%
161153
0.9%
ValueCountFrequency (%)
8525
 
< 0.1%
80110
0.1%
7952
 
< 0.1%
7844
 
< 0.1%
77106
0.1%
7660
 
< 0.1%
7576
 
0.1%
7461
 
< 0.1%
7367
 
0.1%
72248
0.2%

Type of Travel
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.9 MiB
Business travel
89445 
Personal Travel
40042 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBusiness travel
2nd rowBusiness travel
3rd rowBusiness travel
4th rowBusiness travel
5th rowBusiness travel

Common Values

ValueCountFrequency (%)
Business travel89445
69.1%
Personal Travel40042
30.9%

Length

2022-01-08T06:30:24.875783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-08T06:30:24.918879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
travel129487
50.0%
business89445
34.5%
personal40042
 
15.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Class
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.7 MiB
Business
61990 
Eco
58117 
Eco Plus
9380 

Length

Max length8
Median length8
Mean length5.755875107
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEco
2nd rowBusiness
3rd rowEco
4th rowBusiness
5th rowEco

Common Values

ValueCountFrequency (%)
Business61990
47.9%
Eco58117
44.9%
Eco Plus9380
 
7.2%

Length

2022-01-08T06:30:24.971168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-08T06:30:25.016097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
eco67497
48.6%
business61990
44.6%
plus9380
 
6.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Flight Distance
Real number (ℝ≥0)

Distinct3821
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1190.210662
Minimum31
Maximum4983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1011.7 KiB
2022-01-08T06:30:25.072535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile176.3
Q1414
median844
Q31744
95-th percentile3381
Maximum4983
Range4952
Interquartile range (IQR)1330

Descriptive statistics

Standard deviation997.5609542
Coefficient of variation (CV)0.8381381431
Kurtosis0.2659758428
Mean1190.210662
Median Absolute Deviation (MAD)518
Skewness1.108432997
Sum154116808
Variance995127.8574
MonotonicityNot monotonic
2022-01-08T06:30:25.159270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
337840
 
0.6%
594505
 
0.4%
404479
 
0.4%
862472
 
0.4%
2475465
 
0.4%
447456
 
0.4%
236438
 
0.3%
192424
 
0.3%
308402
 
0.3%
214398
 
0.3%
Other values (3811)124608
96.2%
ValueCountFrequency (%)
3111
 
< 0.1%
5611
 
< 0.1%
67160
0.1%
7377
0.1%
7442
 
< 0.1%
762
 
< 0.1%
7756
 
< 0.1%
7837
 
< 0.1%
803
 
< 0.1%
8211
 
< 0.1%
ValueCountFrequency (%)
498316
< 0.1%
496319
< 0.1%
48176
 
< 0.1%
450214
< 0.1%
424323
< 0.1%
400012
< 0.1%
39995
 
< 0.1%
399812
< 0.1%
399712
< 0.1%
399611
< 0.1%

Inflight wifi service
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.728544178
Minimum0
Maximum5
Zeros3908
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1011.7 KiB
2022-01-08T06:30:25.226082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.329234877
Coefficient of variation (CV)0.4871590088
Kurtosis-0.8481420379
Mean2.728544178
Median Absolute Deviation (MAD)1
Skewness0.04040288676
Sum353311
Variance1.766865359
MonotonicityNot monotonic
2022-01-08T06:30:25.284793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
232236
24.9%
332087
24.8%
424702
19.1%
122250
17.2%
514304
11.0%
03908
 
3.0%
ValueCountFrequency (%)
03908
 
3.0%
122250
17.2%
232236
24.9%
332087
24.8%
424702
19.1%
514304
11.0%
ValueCountFrequency (%)
514304
11.0%
424702
19.1%
332087
24.8%
232236
24.9%
122250
17.2%
03908
 
3.0%

Departure/Arrival time convenient
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.057349386
Minimum0
Maximum5
Zeros6664
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size1011.7 KiB
2022-01-08T06:30:25.340634image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.526787221
Coefficient of variation (CV)0.4993826441
Kurtosis-1.041058829
Mean3.057349386
Median Absolute Deviation (MAD)1
Skewness-0.3322959832
Sum395887
Variance2.331079217
MonotonicityNot monotonic
2022-01-08T06:30:25.401048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
431786
24.5%
527906
21.6%
322302
17.2%
221478
16.6%
119351
14.9%
06664
 
5.1%
ValueCountFrequency (%)
06664
 
5.1%
119351
14.9%
221478
16.6%
322302
17.2%
431786
24.5%
527906
21.6%
ValueCountFrequency (%)
527906
21.6%
431786
24.5%
322302
17.2%
221478
16.6%
119351
14.9%
06664
 
5.1%

Ease of Online booking
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.756786396
Minimum0
Maximum5
Zeros5666
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size1011.7 KiB
2022-01-08T06:30:25.457268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.401661946
Coefficient of variation (CV)0.5084405334
Kurtosis-0.913189637
Mean2.756786396
Median Absolute Deviation (MAD)1
Skewness-0.01856230458
Sum356968
Variance1.96465621
MonotonicityNot monotonic
2022-01-08T06:30:25.517190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
330297
23.4%
229983
23.2%
424362
18.8%
121808
16.8%
517371
13.4%
05666
 
4.4%
ValueCountFrequency (%)
05666
 
4.4%
121808
16.8%
229983
23.2%
330297
23.4%
424362
18.8%
517371
13.4%
ValueCountFrequency (%)
517371
13.4%
424362
18.8%
330297
23.4%
229983
23.2%
121808
16.8%
05666
 
4.4%

Gate location
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.976931869
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1011.7 KiB
2022-01-08T06:30:25.577811image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.278479365
Coefficient of variation (CV)0.4294620843
Kurtosis-1.031576714
Mean2.976931869
Median Absolute Deviation (MAD)1
Skewness-0.0582841059
Sum385473.9769
Variance1.634509487
MonotonicityNot monotonic
2022-01-08T06:30:25.639041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
335611
27.5%
430376
23.5%
224219
18.7%
121926
16.9%
517354
13.4%
2.9769088791
 
< 0.1%
ValueCountFrequency (%)
121926
16.9%
224219
18.7%
2.9769088791
 
< 0.1%
335611
27.5%
430376
23.5%
517354
13.4%
ValueCountFrequency (%)
517354
13.4%
430376
23.5%
335611
27.5%
2.9769088791
 
< 0.1%
224219
18.7%
121926
16.9%

Food and drink
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.20468464
Minimum0
Maximum5
Zeros130
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1011.7 KiB
2022-01-08T06:30:25.701870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.329904921
Coefficient of variation (CV)0.4149877665
Kurtosis-1.145461486
Mean3.20468464
Median Absolute Deviation (MAD)1
Skewness-0.1550546438
Sum414965
Variance1.768647099
MonotonicityNot monotonic
2022-01-08T06:30:25.761982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
430477
23.5%
527865
21.5%
327712
21.4%
227293
21.1%
116010
12.4%
0130
 
0.1%
ValueCountFrequency (%)
0130
 
0.1%
116010
12.4%
227293
21.1%
327712
21.4%
430477
23.5%
527865
21.5%
ValueCountFrequency (%)
527865
21.5%
430477
23.5%
327712
21.4%
227293
21.1%
116010
12.4%
0130
 
0.1%

Online boarding
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.329864034
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1011.7 KiB
2022-01-08T06:30:25.821446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3.25272035
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.251950027
Coefficient of variation (CV)0.3759763205
Kurtosis-0.9009701603
Mean3.329864034
Median Absolute Deviation (MAD)0.7472796497
Skewness-0.3430062613
Sum431174.1042
Variance1.567378871
MonotonicityNot monotonic
2022-01-08T06:30:25.884128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
438353
29.6%
327040
20.9%
525941
20.0%
221866
16.9%
113216
 
10.2%
3.252720353071
 
2.4%
ValueCountFrequency (%)
113216
 
10.2%
221866
16.9%
327040
20.9%
3.252720353071
 
2.4%
438353
29.6%
525941
20.0%
ValueCountFrequency (%)
525941
20.0%
438353
29.6%
3.252720353071
 
2.4%
327040
20.9%
221866
16.9%
113216
 
10.2%

Seat comfort
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.44161531
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1011.7 KiB
2022-01-08T06:30:25.950819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.319132959
Coefficient of variation (CV)0.3832889037
Kurtosis-0.9222779693
Mean3.44161531
Median Absolute Deviation (MAD)1
Skewness-0.4861726439
Sum445644.4416
Variance1.740111763
MonotonicityNot monotonic
2022-01-08T06:30:26.013702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
439651
30.6%
533056
25.5%
323258
18.0%
218462
14.3%
115059
 
11.6%
3.4415887311
 
< 0.1%
ValueCountFrequency (%)
115059
 
11.6%
218462
14.3%
323258
18.0%
3.4415887311
 
< 0.1%
439651
30.6%
533056
25.5%
ValueCountFrequency (%)
533056
25.5%
439651
30.6%
3.4415887311
 
< 0.1%
323258
18.0%
218462
14.3%
115059
 
11.6%

Inflight entertainment
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.358533638
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1011.7 KiB
2022-01-08T06:30:26.080064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.333561786
Coefficient of variation (CV)0.3970666756
Kurtosis-1.062857444
Mean3.358533638
Median Absolute Deviation (MAD)1
Skewness-0.3658013488
Sum434886.4452
Variance1.778387038
MonotonicityNot monotonic
2022-01-08T06:30:26.140385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
436682
28.3%
531451
24.3%
323805
18.4%
221897
16.9%
115634
12.1%
3.35806683318
 
< 0.1%
ValueCountFrequency (%)
115634
12.1%
221897
16.9%
323805
18.4%
3.35806683318
 
< 0.1%
436682
28.3%
531451
24.3%
ValueCountFrequency (%)
531451
24.3%
436682
28.3%
3.35806683318
 
< 0.1%
323805
18.4%
221897
16.9%
115634
12.1%

On-board service
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.383204492
Minimum0
Maximum5
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1011.7 KiB
2022-01-08T06:30:26.202210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.287032394
Coefficient of variation (CV)0.3804181499
Kurtosis-0.8885193486
Mean3.383204492
Median Absolute Deviation (MAD)1
Skewness-0.4214850217
Sum438081
Variance1.656452382
MonotonicityNot monotonic
2022-01-08T06:30:26.263557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
438587
29.8%
529407
22.7%
328460
22.0%
218290
14.1%
114738
 
11.4%
05
 
< 0.1%
ValueCountFrequency (%)
05
 
< 0.1%
114738
 
11.4%
218290
14.1%
328460
22.0%
438587
29.8%
529407
22.7%
ValueCountFrequency (%)
529407
22.7%
438587
29.8%
328460
22.0%
218290
14.1%
114738
 
11.4%
05
 
< 0.1%

Leg room service
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.366727162
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1011.7 KiB
2022-01-08T06:30:26.323744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.29625636
Coefficient of variation (CV)0.3850197231
Kurtosis-1.047170336
Mean3.366727162
Median Absolute Deviation (MAD)1
Skewness-0.3214451534
Sum435947.4
Variance1.68028055
MonotonicityNot monotonic
2022-01-08T06:30:26.384813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
435779
27.6%
530815
23.8%
324982
19.3%
224469
18.9%
112846
 
9.9%
3.4596
 
0.5%
ValueCountFrequency (%)
112846
 
9.9%
224469
18.9%
324982
19.3%
3.4596
 
0.5%
435779
27.6%
530815
23.8%
ValueCountFrequency (%)
530815
23.8%
435779
27.6%
3.4596
 
0.5%
324982
19.3%
224469
18.9%
112846
 
9.9%

Baggage handling
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.2 MiB
4
46631 
5
33761 
3
25771 
2
14316 
1
9008 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row3
4th row1
5th row2

Common Values

ValueCountFrequency (%)
446631
36.0%
533761
26.1%
325771
19.9%
214316
 
11.1%
19008
 
7.0%

Length

2022-01-08T06:30:26.453938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-08T06:30:26.497609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
446631
36.0%
533761
26.1%
325771
19.9%
214316
 
11.1%
19008
 
7.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Checkin service
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.306239236
Minimum0
Maximum5
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1011.7 KiB
2022-01-08T06:30:26.542322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.26614617
Coefficient of variation (CV)0.3829566101
Kurtosis-0.8298618171
Mean3.306239236
Median Absolute Deviation (MAD)1
Skewness-0.3665794799
Sum428115
Variance1.603126124
MonotonicityNot monotonic
2022-01-08T06:30:26.602385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
436229
28.0%
335343
27.3%
525800
19.9%
116058
12.4%
216056
12.4%
01
 
< 0.1%
ValueCountFrequency (%)
01
 
< 0.1%
116058
12.4%
216056
12.4%
335343
27.3%
436229
28.0%
525800
19.9%
ValueCountFrequency (%)
525800
19.9%
436229
28.0%
335343
27.3%
216056
12.4%
116058
12.4%
01
 
< 0.1%

Inflight service
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.642373366
Minimum0
Maximum5
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1011.7 KiB
2022-01-08T06:30:26.658519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.176613843
Coefficient of variation (CV)0.323034935
Kurtosis-0.3571576032
Mean3.642373366
Median Absolute Deviation (MAD)1
Skewness-0.6921182799
Sum471640
Variance1.384420137
MonotonicityNot monotonic
2022-01-08T06:30:26.715465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
447198
36.4%
533962
26.2%
325232
19.5%
214252
 
11.0%
18838
 
6.8%
05
 
< 0.1%
ValueCountFrequency (%)
05
 
< 0.1%
18838
 
6.8%
214252
 
11.0%
325232
19.5%
447198
36.4%
533962
26.2%
ValueCountFrequency (%)
533962
26.2%
447198
36.4%
325232
19.5%
214252
 
11.0%
18838
 
6.8%
05
 
< 0.1%

Cleanliness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.286577086
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1011.7 KiB
2022-01-08T06:30:26.772033image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.313179361
Coefficient of variation (CV)0.3995583633
Kurtosis-1.015863098
Mean3.286577086
Median Absolute Deviation (MAD)1
Skewness-0.3003492893
Sum425569.0071
Variance1.724440035
MonotonicityNot monotonic
2022-01-08T06:30:26.833466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
433871
26.2%
330552
23.6%
528321
21.9%
220049
15.5%
116680
12.9%
3.28622178314
 
< 0.1%
ValueCountFrequency (%)
116680
12.9%
220049
15.5%
330552
23.6%
3.28622178314
 
< 0.1%
433871
26.2%
528321
21.9%
ValueCountFrequency (%)
528321
21.9%
433871
26.2%
3.28622178314
 
< 0.1%
330552
23.6%
220049
15.5%
116680
12.9%

Departure Delay in Minutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct464
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.64338505
Minimum0
Maximum1592
Zeros73209
Zeros (%)56.5%
Negative0
Negative (%)0.0%
Memory size1011.7 KiB
2022-01-08T06:30:26.913140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile77
Maximum1592
Range1592
Interquartile range (IQR)12

Descriptive statistics

Standard deviation37.93286655
Coefficient of variation (CV)2.590443837
Kurtosis101.8829471
Mean14.64338505
Median Absolute Deviation (MAD)0
Skewness6.853577956
Sum1896128
Variance1438.902365
MonotonicityNot monotonic
2022-01-08T06:30:26.997108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
073209
56.5%
13671
 
2.8%
22845
 
2.2%
32530
 
2.0%
42298
 
1.8%
52131
 
1.6%
61881
 
1.5%
71745
 
1.3%
81613
 
1.2%
91550
 
1.2%
Other values (454)36014
27.8%
ValueCountFrequency (%)
073209
56.5%
13671
 
2.8%
22845
 
2.2%
32530
 
2.0%
42298
 
1.8%
52131
 
1.6%
61881
 
1.5%
71745
 
1.3%
81613
 
1.2%
91550
 
1.2%
ValueCountFrequency (%)
15921
< 0.1%
13051
< 0.1%
11281
< 0.1%
10171
< 0.1%
9781
< 0.1%
9511
< 0.1%
9331
< 0.1%
9301
< 0.1%
9211
< 0.1%
8591
< 0.1%

Arrival Delay in Minutes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct472
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.09112884
Minimum0
Maximum1584
Zeros72753
Zeros (%)56.2%
Negative0
Negative (%)0.0%
Memory size1011.7 KiB
2022-01-08T06:30:27.086172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q313
95-th percentile78
Maximum1584
Range1584
Interquartile range (IQR)13

Descriptive statistics

Standard deviation38.46565024
Coefficient of variation (CV)2.548891514
Kurtosis95.11711419
Mean15.09112884
Median Absolute Deviation (MAD)0
Skewness6.670124611
Sum1954105
Variance1479.606248
MonotonicityNot monotonic
2022-01-08T06:30:27.170994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
072753
56.2%
12747
 
2.1%
22587
 
2.0%
32442
 
1.9%
42373
 
1.8%
52083
 
1.6%
62021
 
1.6%
71794
 
1.4%
81751
 
1.4%
91566
 
1.2%
Other values (462)37370
28.9%
ValueCountFrequency (%)
072753
56.2%
12747
 
2.1%
22587
 
2.0%
32442
 
1.9%
42373
 
1.8%
52083
 
1.6%
62021
 
1.6%
71794
 
1.4%
81751
 
1.4%
91566
 
1.2%
ValueCountFrequency (%)
15841
< 0.1%
12801
< 0.1%
11151
< 0.1%
10111
< 0.1%
9701
< 0.1%
9521
< 0.1%
9401
< 0.1%
9241
< 0.1%
9201
< 0.1%
8601
< 0.1%

satisfaction
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.1 MiB
neutral or dissatisfied
73225 
satisfied
56262 

Length

Max length23
Median length23
Mean length16.91701097
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsatisfied
2nd rowsatisfied
3rd rowneutral or dissatisfied
4th rowsatisfied
5th rowsatisfied

Common Values

ValueCountFrequency (%)
neutral or dissatisfied73225
56.6%
satisfied56262
43.4%

Length

2022-01-08T06:30:27.253481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-08T06:30:27.298888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
neutral73225
26.5%
or73225
26.5%
dissatisfied73225
26.5%
satisfied56262
20.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-01-08T06:30:21.393103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:50.963640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:52.754550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:54.509524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:56.365121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:58.089716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:59.962780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:01.822024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:03.670974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:05.495831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:07.246786image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:09.083865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:10.807180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:12.467911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:14.350437image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:16.012111image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:17.709751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-01-08T06:30:22.546184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:52.060870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:53.909562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:55.769799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:57.521065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:59.334729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:01.170819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:03.084864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:04.938176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:06.634113image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:08.509589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:10.226821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:11.907279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:13.601691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:15.455963image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:17.113991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:18.833873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:20.591252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:22.648520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:52.281810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:54.013597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:55.868110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:57.618579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:59.433617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:01.355290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:03.183827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:05.035952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:06.734713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:08.608181image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:10.325933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:12.002140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:13.861159image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:15.551369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:17.220646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:18.933065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:20.693577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:22.740058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:52.369926image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:54.110265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:55.958091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:57.707892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:59.523558image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:01.445340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:03.275065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:05.123628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:06.833823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:08.698810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:10.417295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:12.089221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:13.952084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:15.637681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:17.316400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:19.024177image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:20.785439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:22.832054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:52.463072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:54.207283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:56.051580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:57.797300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:59.616500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:01.535354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:03.366829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:05.212241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:06.928224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:08.790025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:10.508465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:12.180458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:14.047101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:15.727120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:17.407680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:19.115185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:20.879682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:22.932049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:52.559159image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:54.309236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:56.151588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:57.895280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:59.715242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:01.629845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:03.465087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:05.306222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:07.029978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:08.887741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:10.608569image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:12.276807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:14.148720image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:15.821939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:17.518755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:19.213151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:20.985708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:23.031952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:52.657062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:54.409514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:56.255239image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:57.992918image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:29:59.819580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:01.728061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:03.562467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:05.403201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:07.144508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:08.985582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:10.707045image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:12.374348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:14.247763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:15.918035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:17.616145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:19.311441image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-08T06:30:21.292476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-01-08T06:30:27.362749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-08T06:30:27.530312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-08T06:30:27.703888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-08T06:30:27.885848image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-01-08T06:30:27.995253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-08T06:30:23.296806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-08T06:30:23.768758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexGenderCustomer TypeAgeType of TravelClassFlight DistanceInflight wifi serviceDeparture/Arrival time convenientEase of Online bookingGate locationFood and drinkOnline boardingSeat comfortInflight entertainmentOn-board serviceLeg room serviceBaggage handlingCheckin serviceInflight serviceCleanlinessDeparture Delay in MinutesArrival Delay in Minutessatisfaction
00FemaleLoyal Customer52Business travelEco1605434.034.03.05.055.05255.05044.0satisfied
11FemaleLoyal Customer36Business travelBusiness28631131.054.05.04.044.04345.000.0satisfied
22Maledisloyal Customer20Business travelEco1922024.022.02.02.041.03222.000.0neutral or dissatisfied
33MaleLoyal Customer44Business travelBusiness33770002.034.04.01.011.01314.006.0satisfied
44FemaleLoyal Customer49Business travelEco11822343.041.02.02.022.02424.0020.0satisfied
55MaleLoyal Customer16Business travelEco3113333.055.03.05.043.01125.000.0satisfied
66FemaleLoyal Customer77Business travelBusiness39875555.035.05.05.055.05453.000.0satisfied
77FemaleLoyal Customer43Business travelBusiness25562222.044.05.04.044.04543.07765.0satisfied
88MaleLoyal Customer47Business travelEco5565222.055.05.05.022.05335.010.0satisfied
99FemaleLoyal Customer46Business travelBusiness17442222.034.04.04.044.04544.02814.0satisfied

Last rows

df_indexGenderCustomer TypeAgeType of TravelClassFlight DistanceInflight wifi serviceDeparture/Arrival time convenientEase of Online bookingGate locationFood and drinkOnline boardingSeat comfortInflight entertainmentOn-board serviceLeg room serviceBaggage handlingCheckin serviceInflight serviceCleanlinessDeparture Delay in MinutesArrival Delay in Minutessatisfaction
129477103894MaleLoyal Customer26Business travelBusiness7124444.055.05.05.034.04345.01726.0satisfied
129478103895Femaledisloyal Customer24Business travelEco10551112.011.01.01.033.05541.01310.0neutral or dissatisfied
129479103896MaleLoyal Customer57Business travelEco8674555.044.04.04.034.03134.000.0neutral or dissatisfied
129480103897FemaleLoyal Customer60Business travelBusiness15995555.055.04.04.044.04444.097.0satisfied
129481103898MaleLoyal Customer50Personal TravelEco16203134.023.02.02.043.04242.000.0neutral or dissatisfied
129482103899Femaledisloyal Customer23Business travelEco1922123.022.02.02.031.04232.030.0neutral or dissatisfied
129483103900MaleLoyal Customer49Business travelBusiness23474444.024.05.05.055.05554.000.0satisfied
129484103901Maledisloyal Customer30Business travelBusiness19951113.041.05.04.032.04554.0714.0neutral or dissatisfied
129485103902Femaledisloyal Customer22Business travelEco10001115.011.01.01.045.01541.000.0neutral or dissatisfied
129486103903MaleLoyal Customer27Business travelBusiness17231333.011.01.01.011.04431.000.0neutral or dissatisfied